Model to dynamically predict patient's discharge readiness in general ward

Abstract

A method for identifying patients for discharge from a general ward in a hospital, including: calculating a transition score of a patient based upon patient vital sign information; computing a TS upper bound value and a TS lower bound value based upon a set of TS values in a TS time window; determining if a length of stay of the patient is greater than a first time window, greater than an expected length of stay, and greater than a lower evaluation window; determining if a current TS lower bound value is less than a lower threshold; and producing an indication that that the patient is to be evaluated for discharge from the general ward when it is determined that the length of stay of the patient is greater than the first time window, greater than the expected length of stay, and greater than the lower evaluation window and that the current TS lower bound value is less than the lower threshold.

What is claimed is: 
     
         1 . A method for identifying patients for discharge from a general ward in a hospital, comprising:
 calculating, by a processor, a transition score of a patient based upon patient vital sign information;   computing a TS upper bound value and a TS lower bound value based upon a set of TS values in a TS time window;   determining if a length of stay of the patient is greater than a first time window, greater than an expected length of stay, and greater than a lower evaluation window;   determining if a current TS lower bound value is less than a lower threshold; and   producing an indication that that the patient is to be evaluated for discharge from the general ward when it is determined that the length of stay of the patient is greater than the first time window, greater than the expected length of stay, and greater than the lower evaluation window and that the current TS lower bound value is less than the lower threshold.   
     
     
         2 . The method of  claim 1 , further comprising producing no recommendation regarding patient discharge when it is determined that the length of stay of the patient is not greater than a first time window, not greater than and expected length of stay, or not greater than a lower evaluation window. 
     
     
         3 . The method of  claim 1 , further comprising producing no recommendation regarding patient discharge when it is determined that that the current TS lower bound value is not less than the lower threshold. 
     
     
         4 . The method of  claim 1 , wherein the first time window has a value in the range of 8 to 24 hours. 
     
     
         5 . The method of  claim 1 , wherein the values of the first window, the lower evaluation window, and the lower threshold are determined by using machine learning techniques with patient training data. 
     
     
         6 . The method of  claim 1 , wherein the transition scores is further based upon diagnostic results, procedures performed, drugs consumed, medical images, or patient demographic information. 
     
     
         7 . The method of  claim 1 , wherein the patient vital signs include heart rate, respiration rate, peripheral capillary oxygen saturation (SpO2), blood pressure, and temperature. 
     
     
         8 . The method of  claim 1 , wherein calculating a transition score of a patient based upon patient vital sign information only occurs when the vital signs were measure within a specified recent period of time. 
     
     
         9 . The method of  claim 1 , further comprising:
 determining if a length of stay of the patient is greater than a second time window and greater than an upper evaluation window;   determining if a current TS upper bound value is greater than an upper threshold; and   producing an indication that that the patient is to be evaluated for a step-up transition from the general ward when it is determined that the length of stay of the patient is greater than the second time window and greater than the lower evaluation window and that the current TS lower bound value is greater than the upper threshold.   
     
     
         10 . The method of  claim 9 , wherein the values of the second window, the upper evaluation window, and the upper threshold are determined by using machine learning techniques with patient training data. 
     
     
         11 . A non-transitory machine-readable storage medium encoded with instructions for identifying patients for discharge from a general ward in a hospital, comprising instructions for:
 calculating a transition score of a patient based upon patient vital sign information;   computing a TS upper bound value and a TS lower bound value based upon a set of TS values in a TS time window;   determining if a length of stay of the patient is greater than a first time window, greater than and expected length of stay, and greater than a lower evaluation window;   determining if a current TS lower bound value is less than a lower threshold; and   producing an indication that that the patient is to be evaluated for discharge from the general ward when it is determined that the length of stay of the patient is greater than the first time window, greater than the expected length of stay, and greater than the lower evaluation window and that the current TS lower bound value is less than the lower threshold.   
     
     
         12 . The non-transitory machine-readable storage medium of  claim 11 , further comprising instructions for producing no recommendation regarding patient discharge when it is determined that the length of stay of the patient is not greater than a first time window, not greater than and expected length of stay, or not greater than a lower evaluation window. 
     
     
         13 . The non-transitory machine-readable storage medium of  claim 11 , further comprising instructions for producing no recommendation regarding patient discharge when it is determined that that the current TS lower bound value is not less than the lower threshold. 
     
     
         14 . The non-transitory machine-readable storage medium of  claim 11 , wherein the first time window has a value in the range of 8 to 24 hours. 
     
     
         15 . The non-transitory machine-readable storage medium of  claim 11 , wherein the values of the first window, the lower evaluation window, and the lower threshold are determined by using machine learning techniques with patient training data. 
     
     
         16 . The non-transitory machine-readable storage medium of  claim 11 , wherein the transition scores is further based upon diagnostic results, procedures performed, drugs consumed, medical images, or patient demographic information. 
     
     
         17 . The non-transitory machine-readable storage medium of  claim 11 , wherein the patient vital signs include heart rate, respiration rate, peripheral capillary oxygen saturation (SpO2), blood pressure, and temperature. 
     
     
         18 . The non-transitory machine-readable storage medium of  claim 11 , wherein calculating a transition score of a patient based upon patient vital sign information only occurs when the vital signs were measure within a specified recent period of time. 
     
     
         19 . The non-transitory machine-readable storage medium of  claim 11 , further comprising instructions for:
 determining if a length of stay of the patient is greater than a second time window and greater than an upper evaluation window;   determining if a current TS upper bound value is greater than an upper threshold; and   producing an indication that that the patient is to be evaluated for a step-up transition from the general ward when it is determined that the length of stay of the patient is greater than the second time window and greater than the lower evaluation window and that the current TS lower bound value is greater than the upper threshold.   
     
     
         20 . The non-transitory machine-readable storage medium of  claim 19 , wherein the values of the second window, the upper evaluation window, and the upper threshold are determined by using machine learning techniques with patient training data.